week-5 Multiple Linear Regression - Program and Explanation

PHOTO

Sun Jun 01 2025 14:47:35 GMT+0000 (Coordinated Universal Time)

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Sample dataset: you can replace this with your own dataset
# For this example, we're assuming a dataset with 3 features
data = {
    'Feature1': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'Feature2': [1, 2, 1, 3, 5, 6, 4, 8, 9, 10],
    'Feature3': [5, 7, 6, 8, 6, 7, 8, 9, 10, 11],
    'Target': [5, 7, 6, 9, 11, 13, 14, 17, 19, 21]
}

# Convert to DataFrame
df = pd.DataFrame(data)

# Features (X) and Target variable (y)
X = df[['Feature1', 'Feature2', 'Feature3']]
y = df['Target']

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create the model
model = LinearRegression()

# Train the model
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f"Mean Squared Error: {mse}")
print(f"R-squared: {r2}")

# Output the coefficients and intercept
print(f"Coefficients: {model.coef_}")
print(f"Intercept: {model.intercept_}")

# Visualizing the results (optional, for 3D)
from mpl_toolkits.mplot3d import Axes3D

# Just a 3D plot for 3 features
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# Scatter plot for actual data
ax.scatter(X_test['Feature1'], X_test['Feature2'], y_test, color='blue', label='Actual data')

# Scatter plot for predicted data
ax.scatter(X_test['Feature1'], X_test['Feature2'], y_pred, color='red', label='Predicted data')

ax.set_xlabel('Feature1')
ax.set_ylabel('Feature2')
ax.set_zlabel('Target')
plt.legend()
plt.show()

COPY

Save snippets that work from anywhere online with our extensions

Comments

More like this

#python ##python #strings #comments

Create simple string along with variables

#assign a value to a variable:
types_of_people = 10 
# make a string using variable name:
X = f “there are {types_of_people} types of people.”

Output:
There are 10 types of people

##python #coding #python #programming #pydantic #uuid

how to make default uuid or id for pydantic model

from pydantic import BaseModel, Field
from uuid import UUID, uuid4

class item(BaseModel):
    name : str
    price : float
    item_id : UUID = Field(default_factory = uuid4)


items = []

for i in range(3):
    items.append(item(name = 'test', price = 20))


print(items)

##python #coding #python

How to Encrypt and Decrypt Data in SQLite with Python's Fernet

from cryptography.fernet import Fernet
import sqlite3

# Generate key to use it
#print(Fernet.generate_key())

key = b'rMqQ4gNSsvkubQnn9CmW25PTFDwNlQPUp7YN4qDVSts='
cipher_suite = Fernet(key)

db_name = 'mydb.db'

conn = sqlite3.connect(db_name)
cursor = conn.cursor()

# create a table
cursor.execute('create table if not exists items (name STRING, price INTEGER)')

def add_item(name, price):
    name = cipher_suite.encrypt(name.encode())
    price = cipher_suite.encrypt(str(price).encode())

    cursor.execute('insert into items (name, price) values (?, ?)', (name, price))

    conn.commit()

add_item('test', 50)

def get_items():
    results = cursor.execute('select * from items').fetchall()
    for item in results:
        name = cipher_suite.decrypt(item[0]).decode()
        price = cipher_suite.decrypt(item[1]).decode()
        print(name, price)

get_items()

#python ##python ##pythonlists

How to use for loop with range function.

  #use for loop and set the range
for index in range(10):
  Print (index)
           
           #when you run this program, the output will be:
               0 
               1 
               2 
               3 
               4 
               5
               6
               7
               8 
               9

##python ##python2.7 ##codecademy

Calculator For Best Shipping Option with Grounded Values

def ground_cost(weight):
  if weight <= 2:
    return weight * 1.50 + 20
  elif weight > 2 and weight <= 6:
    return weight * 3.00 + 20
  elif weight > 6 and weight <= 10:
    return weight * 4.00 + 20
  elif weight > 10:
    return weight * 4.75 + 20

premground_cost = 125.0

def drone_cost(weight):
  if weight <= 2:
    return weight * 4.50
  elif weight > 2 and weight <= 6:
    return weight * 9
  elif weight > 6 and weight <= 10:
    return weight * 12
  elif weight > 10:
    return weight * 14.25


def pricecheck(weight):
  if ground_cost(weight) < premground_cost and ground_cost(weight) < drone_cost(weight):
    return "Ground shipping is the cheapest option for the selected weight. Your total cost will be $" + str(ground_cost(weight)) + "."
  elif premground_cost < ground_cost(weight) and premground_cost < drone_cost(weight):
    return "Our premium option is for you. The total price is set at 125 dollars."
  elif drone_cost(weight) < ground_cost(weight) and drone_cost(weight) < premground_cost:
    return "Drone shipping seems to be the cheapest for you! Your cost will be $" + str(drone_cost(weight)) + "."

##python ##virtualenv ##env

Python virtualenv

# instala la libreria
pip install virtualenv

# genera el virtualenv env
virtualenv env

#activo el virtual env
source env/bin/activate

#guardar todo las librerias en un archivo de texto
pip freeze > requirements.txt

#instalar las librerias de un env a partir de requirements.txt
source env/bin/activate
pip install -r requirements.txt

##python ##outlook

Send an outlook email using python with out SMTP

import win32com.client
from win32com.client import Dispatch, constants

const=win32com.client.constants
olMailItem = 0x0
obj = win32com.client.Dispatch("Outlook.Application")
newMail = obj.CreateItem(olMailItem)
newMail.Subject = "I AM SUBJECT!!"
# newMail.Body = "I AM\nTHE BODY MESSAGE!"
newMail.BodyFormat = 2 # olFormatHTML https://msdn.microsoft.com/en-us/library/office/aa219371(v=office.11).aspx
newMail.HTMLBody = "<HTML><BODY>Enter the <span style='color:red'>message</span> text here.</BODY></HTML>"
newMail.To = "testemail@company.com"
attachment1 = r"C:\Temp\example.pdf"
newMail.Attachments.Add(Source=attachment1)
newMail.display(True)
newMail.Send()

##python ##pandas ##format

f string format

number = 1_000_000.50
f"${number:,.0f}"
'$1,000,000'

f"${number:,.2f}"
'$1,000,000.50'

##python ##for-loop ##if-statements

A prime Checker Code

## Your code should check if each number in the list is a prime number
check_prime = [26, 39, 51, 53, 57, 79, 85]

## write your code here
## HINT: You can use the modulo operator to find a facto
prime_numbers = []
counter_prime = 0
for prime in check_prime:
    if (prime % 2 == 0):
       print("{} is not a prime number".format(prime))
    elif (prime % 3 == 0):
        print("{} is not a prime number".format(prime))
    elif(prime % 5 == 0):
        print("{} is not a prime number".format(prime))
    elif(prime % 7 == 0):
        print("{} is not a prime number".format(prime))
    else:
        prime_numbers.append(prime)
        print("{} is a prime number".format(prime))
    counter_prime += 1

print("These are the prime numbers : {}".format(prime_numbers))



## Altenatively,

check_prime = [26, 39, 51, 53, 57, 79, 85]

# iterate through the check_prime list
for num in check_prime:

# search for factors, iterating through numbers ranging from 2 to the number itself
    for i in range(2, num):

# number is not prime if modulo is 0
        if (num % i) == 0:
            print("{} is NOT a prime number, because {} is a factor of {}".format(num, i, num))
            break

# otherwise keep checking until we've searched all possible factors, and then declare it prime
        if i == num -1:    
            print("{} IS a prime number".format(num))

##python ##imageprocessing

Box Blur | MLPro

import numpy as np

# Please do not change the below function name and parameters
def box_blur(image):
    # print(sum(image))
    image = np.asarray(image)
    image =image.astype(np.float64)
    # print(type(image))
    # print("1")
    h,w = len(image), len(image[0])
    # print(h,w)
    new_arr = np.zeros((h+2,w+2))
    # print(new_arr)
    # print("1")
    for i in range(1,h+1):
        new_arr[i][1:w+1] = image[i-1]
    # print(new_arr)
    # new_arr[1:h+1][1:w+1] = image
    # print(new_arr)

    for i in range(1,h+1):
        for j in range(1,w+1):
            image[i-1][j-1] = (sum(new_arr[i-1][j-1:j+2])+ sum(new_arr[i][j-1:j+2])+sum(new_arr[i+1][j-1:j+2]))/9
            # print(image[i-1][j-1])
    print(image)
    return image

##python ##for-loop ##if-statements

Installing Virtualenv on Windows, MacOX, Linux

STEP 1: UNIVERSAL INSTALL THE VIRTUAL ENV PACKAGE ON YOUR TERMINA
$ pip install virtualenv // this installs the virtualenv

STEP 2: MOVE TO THE DIRECTORY OF YOUR CHOICE 
$ cd Documents/chisomloius/Data-labelling //for example


STEP 3: CREATE THE VIRTUAL ENV NAME OF YOUR CHOICE //
$ virtualenv [name of virtual environment] or python -m virtualenv [name of virtual environment] //this create a directory with the name e.g virtual_env
$ virtualenv env or python -m virtualenv virtual_env //this create a directory with the name 'virtual_env'


STEP 4: ACTIVATE/DEACTIVATE VIRTUAL ENV
$ .\/virtual_env/scripts/activate // use this to activate for command prompt
$ .\/virtual_env/Scripts/activate.ps1 // use this to activate for Powershell
$ source bin/activate // use this to MacOSX and Linux
---------------------------------
$ deactivate // deactiate virtaul env

STEP 5: ADDING OTHER PACKAGES ON THE VIRTUAL ENVIRONMENT OR USING THE REQUIREMENT FILE
pip install -r requirements.txt //import more than one package
pip install pageName

##python ##machinelearning

Box-Muller Transformation

#!/usr/bin/python3

import numpy as np

# Please do not change the below function name and parameters
def transform(u1,u2):
    z1 = np.sqrt(-2* np.log(u1)) * np.cos(2*np.pi*u2)
    z2 = np.sqrt(-2* np.log(u1)) * np.sin(2*np.pi*u2)
    return (z1,z2)

##python ##machinelearning

Bubble Sort | MLPro

def bubbleSort(values):
    for i in range(len(values)):
        for j in range(len(values)-1):
            if values[j] > values[j+1]:
                values[j] ,values[j+1] = values[j+1], values[j]
    return values

##pandas ##python

find the percentage of missing values in pandas

percent_missing = df.isnull().sum() * 100 / len(df)
missing_value_df = pd.DataFrame({'column_name': df.columns,
                                 'percent_missing': percent_missing})

##flask ##python

Flask Basic Configuration

from flask import Flask, redirect, url_for, request, render_template, flash

app = Flask(__name__)
  
@app.route('/', methods = ['GET'])
def index():
    return render_template('index.html')
  
if __name__ == '__main__':
    app.run()

week-5 Multiple Linear Regression - Program and Explanation

Save snippets that work from anywhere online with our extensions

Comments

More like this

Browse more snippets >>

Embed code snippet